Randomization inference procedures for simple and complex randomized designs, including multi-armed trials, as described in Gerber and Green (2012, ISBN: 978-0393979954). Users formally describe their randomization procedure and test statistic. The randomization distribution of the test statistic under some null hypothesis is efficiently simulated.

Designed for longitudinal data analysis using Hidden Markov Models (HMMs). Tailored for applications in healthcare, social sciences, and economics, the main emphasis of this package is on regularization techniques for fitting HMMs. Additionally, it provides an implementation for fitting HMMs without regularization, referencing Zucchini et al. (2017, ISBN:9781315372488).

It is a package that provides alternative approach for finding optimum parameters of ridge regression. This package focuses on finding the ridge parameter value k which makes the variance inflation factors closest to 1, while keeping them above 1 as addressed by Michael Kutner, Christopher Nachtsheim, John Neter, William Li (2004, ISBN:978-0073108742). Moreover, the package offers end-to-end functionality to find optimum k value and presents the detailed ridge regression results. Finally it shows three sets of graphs consisting k versus variance inflation factors, regression coefficients and standard errors of them.

Estimation of reproduction numbers for disease outbreak, based on incidence data. The R0 package implements several documented methods. It is therefore possible to compare estimations according to the methods used. Depending on the methods requested by user, basic reproduction number (commonly denoted as R0) or real-time reproduction number (referred to as R(t)) is computed, along with a 95% Confidence Interval. Plotting outputs will give different graphs depending on the methods requested : basic reproductive number estimations will only show the epidemic curve (collected data) and an adjusted model, whereas real-time methods will also show the R(t) variations throughout the outbreak time period. Sensitivity analysis tools are also provided, and allow for investigating effects of varying Generation Time distribution or time window on estimates.

These functions take a character vector as input, identify and cluster similar values, and then merge clusters together so their values become identical. The functions are an implementation of the key collision and ngram fingerprint algorithms from the open source tool Open Refine <https://openrefine.org/>. More info on key collision and ngram fingerprint can be found here <https://openrefine.org/docs/technical-reference/clustering-in-depth>.

This package provides a client for the API of OpenDota. OpenDota is a web service which is provide DOTA2 real time data. Data is collected through the Steam WebAPI. With ROpenDota you can easily grab the latest DOTA2 statistics in R programming such as latest match on official international competition, analyzing your or enemy performance to learn their strategies,etc. Please see <https://github.com/rosdyana/ROpenDota> for more information.

Principal curves generalize the notion of a first principal component to the case in which it is a nonlinear smooth curve. This package provides a function pcop(X) to compute principal curves with the algorithm defined in Delicado (2001) <doi:10.1006/jmva.2000.1917> from a data matrix X.

This package provides a model of single-layer groundwater flow in steady-state under the Dupuit-Forchheimer assumption can be created by placing elements such as wells, area-sinks and line-sinks at arbitrary locations in the flow field. Output variables include hydraulic head and the discharge vector. Particle traces can be computed numerically in three dimensions. The underlying theory is described in Haitjema (1995) <doi:10.1016/B978-0-12-316550-3.X5000-4> and references therein.

This package provides typed parameter documentation tags for integration with roxygen2'. Typed parameter tags provide a consistent interface for annotating expected types for parameters and returned values. Tools for converting from existing styles are also provided to easily adapt projects which implement typed documentation by convention rather than tag. Use the default format or provide your own.

This package provides functions to download and parse robots.txt files. Ultimately the package makes it easy to check if bots (spiders, crawler, scrapers, ...) are allowed to access specific resources on a domain.

This package provides pre-compiled static VTK libraries and headers so that downstream R packages can link against the Visualization Toolkit without requiring users to install VTK manually. On all platforms the package first honours a user-supplied VTK_DIR environment variable. On macOS it then tries Homebrew', followed by pkg-config'. On Linux it tries pkg-config and well-known system prefixes ('/usr', /usr/local'). If no suitable system installation is found on macOS or Linux, pre-built static libraries are downloaded automatically from the package's GitHub releases. On Windows the package tries VTK_DIR', then Rtools45 pacman', then common MSYS2 prefixes, accepting both static ('.a') and shared ('.dll.a import libs + DLLs) installations. When shared libraries are used, the VTK DLLs are staged in inst/vtk-dlls/ and an .onLoad hook prepends that directory to PATH via Sys.setenv() when the package is loaded, and restored in .onUnload()'. The pre-built fallback downloads static libraries by default; set VTK_LINK_TYPE=shared before installation to download the DLL build instead. Note that on Windows the modules VTK_IONetCDF', VTK_IOHDF', VTK_GeovisCore', and VTK_RenderingCore are disabled because netcdf and libproj are not available in the Rtools45 static.posix sysroot. Downstream packages can declare Imports: rvtk and obtain the correct compiler and linker flags at install time via rvtk::CppFlags() and rvtk::LdFlagsFile().

This package provides functions to query occurrence records, species information, and datasets from BIODATACR <https://biodiversidad.go.cr>, the national biodiversity information platform of Costa Rica managed by the Technical Office of CONAGEBIO, Costa Rica. Built on the Atlas of Living Australia (ALA) API infrastructure.

Estimates of standard errors of popular risk and performance measures for asset or portfolio returns using methods as described in Chen and Martin (2021) <doi:10.21314/JOR.2020.446>.

Interface to the yacas computer algebra system (<http://www.yacas.org/>).

Regression methods to quantify the relation between two measurement methods are provided by this package. The focus is on a Bayesian Deming regressions family. With a Bayesian method the Deming regression can be run in a traditional fashion or can be run in a robust way just decreasing the degree of freedom d.f. of the sampling distribution. With d.f. = 1 an extremely robust Cauchy distribution can be sampled. Moreover, models for dealing with heteroscedastic data are also provided. For reference see G. Pioda (2024) <https://piodag.github.io/bd1/>.

Estimates the rank intraclass correlation coefficient (ICC) for clustered continuous and ordinal data. See Tu et al. (2023) <DOI:10.1002/sim.9864> for details.

Fit and simulate any kind of physiologically-based kinetic ('PBK') models whatever the number of compartments. Moreover, it allows to account for any link between pairs of compartments, as well as any link of each of the compartments with the external medium. Such generic PBK models have today applications in pharmacology (PBPK models) to describe drug effects, in toxicology and ecotoxicology (PBTK models) to describe chemical substance effects. In case of exposure to a parent compound (drug or chemical) the rPBK package allows to consider metabolites, whatever their number and their phase (I, II, ...). Last but not least, package rPBK can also be used for dynamic flux balance analysis (dFBA) to deal with metabolic networks. See also Charles et al. (2022) <doi:10.1101/2022.04.29.490045>.

This package provides a piped query generator based on Edgar F. Codd's relational algebra, and on production experience using SQL and dplyr at big data scale. The design represents an attempt to make SQL more teachable by denoting composition by a sequential pipeline notation instead of nested queries or functions. The implementation delivers reliable high performance data processing on large data systems such as Spark', databases, and data.table'. Package features include: data processing trees or pipelines as observable objects (able to report both columns produced and columns used), optimized SQL generation as an explicit user visible table modeling step, plus explicit query reasoning and checking.

Enhances the R Optimization Infrastructure ('ROI') package with the clarabel solver for solving convex cone problems. More information about clarabel can be found at <https://oxfordcontrol.github.io/ClarabelDocs/stable/>.

An easy way to get started with Generative Adversarial Nets (GAN) in R. The GAN algorithm was initially described by Goodfellow et al. 2014 <https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf>. A GAN can be used to learn the joint distribution of complex data by comparison. A GAN consists of two neural networks a Generator and a Discriminator, where the two neural networks play an adversarial minimax game. Built-in GAN models make the training of GANs in R possible in one line and make it easy to experiment with different design choices (e.g. different network architectures, value functions, optimizers). The built-in GAN models work with tabular data (e.g. to produce synthetic data) and image data. Methods to post-process the output of GAN models to enhance the quality of samples are available.

This package provides a pair of functions for calculating mean residual life (MRL) , median residual life, and percentile residual life using the outputs of either the flexsurv package or parameters provided by the user. Input information about the distribution, the given life value, the percentile, and the type of residual life, and the function will return your desired values. For the flexsurv option, the function allows the user to input their own data for making predictions. This function is based on Jackson (2016) <doi:10.18637/jss.v070.i08>.

Robust inference methods for fixed-effect and random-effects models of meta-analysis are implementable. The robust methods are developed using the density power divergence that is a robust estimating criterion developed in machine learning theory, and can effectively circumvent biases and misleading results caused by influential outliers. The density power divergence is originally introduced by Basu et al. (1998) <doi:10.1093/biomet/85.3.549>, and the meta-analysis methods are developed by Noma et al. (2022) <forthcoming>.

Analysis of corneal data obtained from a Placido disk corneal topographer with calculation of irregularity indices. This package performs analyses of corneal data obtained from a Placido disk corneal topographer, with the calculation of the Placido irregularity indices and the posterior analysis. The package is intended to be easy to use by a practitioner, providing a simple interface and yielding easily interpretable results. A corneal topographer is an ophthalmic clinical device that obtains measurements in the cornea (the anterior part of the eye). A Placido disk corneal topographer makes use of the Placido disk [Rowsey et al. (1981)]<doi:10.1001/archopht.1981.03930011093022>, which produce a circular pattern of measurement nodes. The raw information measured by such a topographer is used by practitioners to analyze curvatures, to study optical aberrations, or to diagnose specific conditions of the eye (e.g. keratoconus, an important corneal disease). The rPACI package allows the calculation of the corneal irregularity indices described in [Castro-Luna et al. (2020)]<doi:10.1016%2Fj.clae.2019.12.006>, [Ramos-Lopez et al. (2013)]<doi:10.1097%2FOPX.0b013e3182843f2a>, and [Ramos-Lopez et al. (2011)]<doi:10.1097/opx.0b013e3182279ff8>. It provides a simple interface to read corneal topography data files as exported by a typical Placido disk topographer, to compute the irregularity indices mentioned before, and to display summary plots that are easy to interpret for a clinician.

Implementation of the affine-invariant method of Goodman & Weare (2010) <DOI:10.2140/camcos.2010.5.65>, a method of producing Monte-Carlo samples from a target distribution.